RD-Agent Tutorial - Chapter 3: Advanced Usage Techniques

3.1 Quantitative Finance Agent Deep Dive

Quantitative Finance Scenario Architecture

RD-Agent's quantitative finance agent RD-Agent(Q) is the industry's first data-driven multi-agent quantitative strategy development framework. It achieves full-stack quantitative research and development automation through coordinated factor-model joint optimization.

Core Architecture Design

graph TB
    A[Market Data] --> B[Data Preprocessing]
    B --> C[Factor Mining Agent]
    B --> D[Model Evolution Agent]
    C --> E[Factor Evaluation]
    D --> F[Model Evaluation]
    E --> G[Joint Optimization Agent]
    F --> G
    G --> H[Strategy Backtesting]
    H --> I[Performance Analysis]
    I --> J{Requirements Met?}
    J -->|No| K[Knowledge Feedback]
    K --> C
    K --> D
    J -->|Yes| L[Strategy Deployment]

    style C fill:#e1f5fe
    style D fill:#f3e5f5
    style G fill:#e8f5e8
    style H fill:#fff3e0

Qlib Framework Integration

RD-Agent(Q) deeply integrates with Microsoft's Qlib quantitative investment library, providing:

Data Management:

• 🗃️ Standardized financial data format
• 📈 Real-time and historical data interfaces
• 🔄 Automatic data update mechanism
• 🧹 Data cleaning and preprocessing

Backtesting Engine:

• 📊 High-performance vectorized backtesting
• 💰 Trading cost and slippage simulation
• 📋 Multiple portfolio construction strategies
• 📈 Risk metric calculation

Model Library:

• 🤖 Machine learning model integration
• 📊 Traditional quantitative factors
• 🧠 Deep learning models
• 🔄 Model ensemble strategies

Factor Mining and Optimization

rdagent fin_factor Explained

The factor mining agent focuses on discovering and optimizing effective quantitative factors.

Startup Command:

# Basic factor mining
rdagent fin_factor

# Specify configuration file
rdagent fin_factor --config factor_config.yaml

# Specify iteration count
export FACTOR_MAX_LOOP=15
rdagent fin_factor

Workflow:

1. Hypothesis Generation Phase

   # Generate factor hypotheses based on market theory and historical experience
   factor_hypotheses = [
       "technical_indicator_momentum_factor",
       "financial_quality_factor",
       "market_sentiment_factor",
       "macroeconomic_factor"
   ]

1. Factor Implementation Phase

   # Automatically generate factor calculation code
   class MomentumFactor:
       def calculate(self, data):
           # Auto-generated momentum factor calculation logic
           return (data['close'] / data['close'].shift(20) - 1)

1. Factor Evaluation Phase

   # Multi-dimensional factor evaluation
   evaluation_metrics = {
       'IC': 0.045,          # Information Coefficient
       'IC_IR': 1.2,         # Information Ratio
       'rank_IC': 0.038,     # Rank IC
       'turnover': 0.8,      # Turnover rate
       'max_drawdown': 0.15  # Maximum drawdown
   }

Factor Generation Strategies

Technical Analysis Factors:

technical_factors = {
    "momentum": [
        "price_momentum_5d", "price_momentum_20d",
        "volume_momentum", "volatility_momentum"
    ],
    "mean_reversion": [
        "rsi_divergence", "bollinger_position",
        "price_deviation"
    ],
    "trend": [
        "ma_trend", "macd_signal", "trend_strength"
    ]
}

Fundamental Factors:

fundamental_factors = {
    "profitability": [
        "roe_trend", "roa_improvement",
        "gross_margin_stability"
    ],
    "growth": [
        "revenue_growth_consistency", "eps_growth_quality",
        "cash_flow_growth"
    ],
    "valuation": [
        "pe_relative", "pb_sector_adjusted",
        "ev_ebitda_normalized"
    ]
}

Alternative Factors:

alternative_factors = {
    "sentiment": [
        "news_sentiment_score", "social_media_buzz",
        "analyst_revision_momentum"
    ],
    "network": [
        "supply_chain_strength", "industry_correlation",
        "peer_performance_influence"
    ]
}

Factor Effectiveness Evaluation

Evaluation Framework:

class FactorEvaluator:
    def __init__(self, benchmark_data, start_date, end_date):
        self.data = benchmark_data
        self.start_date = start_date
        self.end_date = end_date

    def evaluate_factor(self, factor_values):
        """Comprehensive factor effectiveness evaluation"""
        metrics = {}

        # 1. Information Coefficient analysis
        metrics['IC'] = self.calculate_IC(factor_values)
        metrics['IC_std'] = self.calculate_IC_stability(factor_values)
        metrics['IC_IR'] = metrics['IC'] / metrics['IC_std']

        # 2. Monotonicity test
        metrics['monotonicity'] = self.test_monotonicity(factor_values)

        # 3. Turnover rate analysis
        metrics['turnover'] = self.calculate_turnover(factor_values)

        # 4. Backtesting performance
        backtest_result = self.backtest_factor(factor_values)
        metrics.update(backtest_result)

        return metrics

Model Evolution and Optimization

rdagent fin_model Explained

The model evolution agent focuses on automated development and optimization of prediction models.

Supported Model Types:

Model Category	Specific Models	Applicable Scenarios	Features
Linear Models	LinearRegression, Ridge, Lasso	Simple fast prediction	High interpretability
Tree Models	XGBoost, LightGBM, CatBoost	Tabular data modeling	Feature importance
Neural Networks	MLP, TabNet, DeepFM	Complex relationship modeling	Non-linear fitting
Time Series Models	LSTM, GRU, Transformer	Sequence data prediction	Time dependency
Ensemble Models	Stacking, Voting, Blending	Improve prediction accuracy	Model fusion

Model Architecture Search

Automated Architecture Search:

class ModelArchitectureSearch:
    def __init__(self):
        self.search_space = {
            "n_layers": [2, 3, 4, 5],
            "hidden_dims": [64, 128, 256, 512],
            "dropout_rate": [0.1, 0.2, 0.3, 0.4],
            "activation": ["relu", "tanh", "gelu"],
            "optimizer": ["adam", "adamw", "sgd"],
            "learning_rate": [0.001, 0.01, 0.1]
        }

    def search_best_architecture(self, train_data, valid_data):
        """Search for optimal model architecture"""
        best_config = None
        best_score = float('-inf')

        for config in self.generate_configurations():
            model = self.build_model(config)
            score = self.evaluate_model(model, train_data, valid_data)

            if score > best_score:
                best_score = score
                best_config = config

        return best_config, best_score

Hyperparameter Optimization

Multiple Optimization Algorithms:

from optuna import create_study
from sklearn.model_selection import cross_val_score

class HyperparameterOptimizer:
    def __init__(self, model_class, param_space):
        self.model_class = model_class
        self.param_space = param_space

    def optimize_with_optuna(self, X_train, y_train, n_trials=100):
        """Hyperparameter optimization using Optuna"""
        def objective(trial):
            params = {}
            for param_name, param_range in self.param_space.items():
                if isinstance(param_range, list):
                    params[param_name] = trial.suggest_categorical(
                        param_name, param_range
                    )
                elif isinstance(param_range, tuple):
                    if isinstance(param_range[0], int):
                        params[param_name] = trial.suggest_int(
                            param_name, param_range[0], param_range[1]
                        )
                    else:
                        params[param_name] = trial.suggest_float(
                            param_name, param_range[0], param_range[1]
                        )

            model = self.model_class(**params)
            scores = cross_val_score(model, X_train, y_train, cv=5)
            return scores.mean()

        study = create_study(direction='maximize')
        study.optimize(objective, n_trials=n_trials)

        return study.best_params, study.best_value

Factor-Model Joint Optimization

rdagent fin_quant Explained

The joint optimization agent is the core innovation of RD-Agent(Q), implementing collaborative evolution of factors and models.

Joint Optimization Strategies:

1. Alternating Optimization Strategy

   class AlternatingOptimization:
       def __init__(self, factor_agent, model_agent):
           self.factor_agent = factor_agent
           self.model_agent = model_agent

       def optimize(self, max_iterations=10):
           for i in range(max_iterations):
               # Fix model, optimize factors
               new_factors = self.factor_agent.evolve_factors(
                   current_model=self.current_model
               )

               # Fix factors, optimize model
               new_model = self.model_agent.evolve_model(
                   current_factors=new_factors
               )

               # Evaluate joint performance
               performance = self.evaluate_joint_performance(
                   new_factors, new_model
               )

               if performance > self.best_performance:
                   self.update_best_solution(new_factors, new_model)

1. Multi-Objective Optimization Strategy

   class MultiObjectiveOptimization:
       def __init__(self):
           self.objectives = [
               "maximize_sharpe_ratio",
               "minimize_max_drawdown",
               "maximize_information_ratio",
               "minimize_turnover"
           ]

       def pareto_optimization(self, population):
           """Pareto front optimization"""
           pareto_front = []

           for individual in population:
               is_dominated = False
               for other in population:
                   if self.dominates(other, individual):
                       is_dominated = True
                       break

               if not is_dominated:
                   pareto_front.append(individual)

           return pareto_front

Multi-Objective Optimization Algorithms

NSGA-II Implementation:

class NSGA2Optimizer:
    def __init__(self, population_size=100, generations=50):
        self.population_size = population_size
        self.generations = generations

    def optimize(self, factor_space, model_space):
        # Initialize population
        population = self.initialize_population()

        for generation in range(self.generations):
            # Evaluate fitness
            fitness_values = self.evaluate_population(population)

            # Non-dominated sorting
            fronts = self.fast_non_dominated_sort(population, fitness_values)

            # Select next generation
            next_population = self.select_next_generation(
                fronts, population, fitness_values
            )

            # Crossover and mutation
            offspring = self.crossover_and_mutation(next_population)
            population = offspring

        return self.get_pareto_front(population)

Collaborative Evolution Mechanism

Knowledge Sharing Mechanism:

class KnowledgeSharing:
    def __init__(self):
        self.factor_knowledge_base = {}
        self.model_knowledge_base = {}
        self.interaction_patterns = {}

    def share_factor_insights(self, factor_id, insights):
        """Share factor insights"""
        self.factor_knowledge_base[factor_id] = {
            'effectiveness': insights['ic_score'],
            'stability': insights['ic_std'],
            'best_models': insights['compatible_models'],
            'market_regimes': insights['effective_periods']
        }

    def share_model_insights(self, model_id, insights):
        """Share model insights"""
        self.model_knowledge_base[model_id] = {
            'architecture': insights['model_config'],
            'performance': insights['validation_score'],
            'best_factors': insights['important_features'],
            'hyperparameters': insights['optimal_params']
        }

    def get_recommendations(self, current_factors, current_model):
        """Get optimization recommendations"""
        recommendations = {
            'factor_suggestions': [],
            'model_suggestions': [],
            'joint_strategies': []
        }

        # Generate recommendations based on historical knowledge
        for factor in current_factors:
            if factor in self.factor_knowledge_base:
                knowledge = self.factor_knowledge_base[factor]
                recommendations['model_suggestions'].extend(
                    knowledge['best_models']
                )

        return recommendations

Financial Report Analysis

rdagent fin_factor_report Usage

Automatically extracting quantitative factors from financial reports is a unique capability of RD-Agent.

Supported Report Types:

• 📋 Annual Reports (10-K, Annual Report)
• 📊 Quarterly Reports (10-Q, Quarterly Report)
• 📰 News Announcements (8-K, Press Release)
• 📈 Analyst Reports (Research Report)
• 🏛️ Regulatory Filings (SEC Filings)

Usage Example:

# Basic report analysis
rdagent fin_factor_report --report-folder ./financial_reports

# Specify report type
rdagent fin_factor_report \
    --report-folder ./reports \
    --report-type annual \
    --language zh

# Batch processing
rdagent fin_factor_report \
    --report-folder ./reports \
    --batch-size 50 \
    --parallel-workers 4

Report Parsing and Feature Extraction

Text Preprocessing Pipeline:

class ReportPreprocessor:
    def __init__(self):
        self.nlp = spacy.load("en_core_web_sm")
        self.financial_terms = self.load_financial_dictionary()

    def preprocess_report(self, report_text):
        """Preprocess financial report"""
        # 1. Document cleaning
        cleaned_text = self.clean_document(report_text)

        # 2. Section segmentation
        sections = self.extract_sections(cleaned_text)

        # 3. Entity recognition
        entities = self.extract_financial_entities(sections)

        # 4. Sentiment analysis
        sentiment_scores = self.analyze_sentiment(sections)

        return {
            'sections': sections,
            'entities': entities,
            'sentiment': sentiment_scores,
            'metadata': self.extract_metadata(report_text)
        }

Factor Extraction Strategy:

class FinancialFactorExtractor:
    def __init__(self):
        self.factor_extractors = {
            'growth_factors': GrowthFactorExtractor(),
            'quality_factors': QualityFactorExtractor(),
            'sentiment_factors': SentimentFactorExtractor(),
            'risk_factors': RiskFactorExtractor()
        }

    def extract_factors(self, processed_report):
        """Extract factors from processed report"""
        extracted_factors = {}

        for factor_type, extractor in self.factor_extractors.items():
            factors = extractor.extract(processed_report)
            extracted_factors[factor_type] = factors

        return extracted_factors

class GrowthFactorExtractor:
    def extract(self, report_data):
        """Extract growth factors"""
        growth_factors = {}

        # Revenue growth quality
        revenue_mentions = self.find_revenue_discussions(report_data)
        growth_factors['revenue_growth_quality'] = self.analyze_growth_quality(
            revenue_mentions
        )

        # Market expansion strategy
        expansion_mentions = self.find_expansion_discussions(report_data)
        growth_factors['expansion_strategy_score'] = self.score_expansion_strategy(
            expansion_mentions
        )

        # R&D investment commitment
        rd_mentions = self.find_rd_discussions(report_data)
        growth_factors['rd_commitment_score'] = self.analyze_rd_commitment(
            rd_mentions
        )

        return growth_factors

Sentiment Analysis and Signal Mining

Multi-dimensional Sentiment Analysis:

class FinancialSentimentAnalyzer:
    def __init__(self):
        self.sentiment_model = pipeline(
            "sentiment-analysis",
            model="ProsusAI/finbert"
        )
        self.uncertainty_detector = UncertaintyDetector()
        self.forward_looking_detector = ForwardLookingDetector()

    def analyze_comprehensive_sentiment(self, text_sections):
        """Comprehensive sentiment analysis"""
        sentiment_scores = {}

        for section_name, text in text_sections.items():
            # Basic sentiment analysis
            basic_sentiment = self.sentiment_model(text)

            # Uncertainty detection
            uncertainty_score = self.uncertainty_detector.detect(text)

            # Forward-looking statement detection
            forward_looking = self.forward_looking_detector.detect(text)

            sentiment_scores[section_name] = {
                'polarity': basic_sentiment[0]['score'],
                'label': basic_sentiment[0]['label'],
                'uncertainty': uncertainty_score,
                'forward_looking': forward_looking,
                'confidence': self.calculate_confidence(
                    basic_sentiment, uncertainty_score
                )
            }

        return sentiment_scores

class SignalGenerator:
    def __init__(self):
        self.signal_weights = {
            'management_tone': 0.3,
            'financial_health': 0.4,
            'market_position': 0.2,
            'future_outlook': 0.1
        }

    def generate_trading_signals(self, factor_scores, sentiment_scores):
        """Generate trading signals"""
        composite_score = 0.0

        # Weighted composite score
        for factor_type, weight in self.signal_weights.items():
            if factor_type in factor_scores:
                composite_score += factor_scores[factor_type] * weight

        # Sentiment adjustment
        sentiment_adjustment = self.calculate_sentiment_adjustment(
            sentiment_scores
        )

        final_score = composite_score * sentiment_adjustment

        # Generate signal
        if final_score > 0.6:
            signal = "STRONG_BUY"
        elif final_score > 0.3:
            signal = "BUY"
        elif final_score > -0.3:
            signal = "HOLD"
        elif final_score > -0.6:
            signal = "SELL"
        else:
            signal = "STRONG_SELL"

        return {
            'signal': signal,
            'confidence': abs(final_score),
            'score': final_score,
            'components': {
                'factor_score': composite_score,
                'sentiment_adjustment': sentiment_adjustment
            }
        }

3.2 CoSTEER Evolution Framework Deep Dive

Evolution Framework Core Principles

CoSTEER (Collaborative evolving STrategy for automatic data-cEntric dEvelopment fRamework) is RD-Agent's core evolution engine, implementing autonomous learning and continuous improvement for agents.

Collaborative Evolution Strategy Design

The CoSTEER framework is based on the following core principles:

1. Multi-Agent Collaboration - Different specialized agents work together
2. Knowledge Accumulation - Learn and improve from historical experience
3. Adaptive Evolution - Dynamically adjust strategies based on feedback
4. RAG Enhancement - Retrieval-augmented generation improves decision quality

class CoSTEER(Developer[Experiment]):
    """CoSTEER evolution framework core class"""

    def __init__(
        self,
        settings: CoSTEERSettings,
        eva: RAGEvaluator,
        es: EvolvingStrategy,
        evolving_version: int = 2,
        with_knowledge: bool = True,
        knowledge_self_gen: bool = True,
        max_loop: int | None = None,
    ):
        self.settings = settings
        self.max_loop = settings.max_loop if max_loop is None else max_loop
        self.knowledge_base_path = Path(settings.knowledge_base_path)
        self.with_knowledge = with_knowledge
        self.knowledge_self_gen = knowledge_self_gen
        self.evolving_strategy = es
        self.evaluator = eva

        # Initialize RAG system
        self.rag_strategy = self._init_rag_strategy(evolving_version)

Knowledge Management Mechanism

Multi-level Knowledge Representation:

class KnowledgeManagementSystem:
    def __init__(self):
        self.knowledge_layers = {
            'factual_knowledge': FactualKnowledgeBase(),
            'procedural_knowledge': ProceduralKnowledgeBase(),
            'experiential_knowledge': ExperientialKnowledgeBase(),
            'meta_knowledge': MetaKnowledgeBase()
        }

    def store_knowledge(self, knowledge_item):
        """Layered knowledge storage"""
        knowledge_type = self.classify_knowledge(knowledge_item)
        self.knowledge_layers[knowledge_type].store(knowledge_item)

    def retrieve_relevant_knowledge(self, query_context):
        """Retrieve relevant knowledge"""
        relevant_knowledge = {}

        for layer_name, knowledge_base in self.knowledge_layers.items():
            relevant_items = knowledge_base.query(
                query_context,
                similarity_threshold=0.7
            )
            relevant_knowledge[layer_name] = relevant_items

        return self.synthesize_knowledge(relevant_knowledge)

Knowledge Representation Format:

@dataclass
class KnowledgeItem:
    """Knowledge item data structure"""
    id: str
    content: str
    knowledge_type: KnowledgeType
    domain: str
    confidence: float
    source: str
    timestamp: datetime
    usage_count: int = 0
    success_rate: float = 0.0

    def to_embedding(self) -> np.ndarray:
        """Convert to vector representation"""
        return self.embedding_model.encode(self.content)

    def update_effectiveness(self, success: bool):
        """Update knowledge effectiveness"""
        self.usage_count += 1
        if success:
            self.success_rate = (
                (self.success_rate * (self.usage_count - 1) + 1.0)
                / self.usage_count
            )
        else:
            self.success_rate = (
                self.success_rate * (self.usage_count - 1)
                / self.usage_count
            )

Adaptive Optimization Algorithm

Multi-Strategy Evolution Algorithm:

class AdaptiveEvolutionStrategy:
    def __init__(self):
        self.evolution_strategies = {
            'genetic_algorithm': GeneticAlgorithmStrategy(),
            'differential_evolution': DifferentialEvolutionStrategy(),
            'particle_swarm': ParticleSwarmStrategy(),
            'simulated_annealing': SimulatedAnnealingStrategy()
        }
        self.strategy_performance = {}
        self.adaptive_weights = {}

    def evolve_population(self, population, generation):
        """Adaptive population evolution"""
        # Select evolution strategy
        selected_strategies = self.select_strategies(generation)

        new_population = []
        for strategy_name, weight in selected_strategies.items():
            strategy = self.evolution_strategies[strategy_name]

            # Allocate population by weight
            sub_population_size = int(len(population) * weight)
            sub_population = population[:sub_population_size]

            # Evolve sub-population
            evolved_sub_pop = strategy.evolve(sub_population)
            new_population.extend(evolved_sub_pop)

        # Update strategy performance
        self.update_strategy_performance(new_population, generation)

        return new_population

    def select_strategies(self, generation):
        """Select evolution strategy based on historical performance"""
        if generation < 5:
            # Uniform distribution in early stages
            return {name: 1.0/len(self.evolution_strategies)
                   for name in self.evolution_strategies.keys()}

        # Adjust weights based on performance
        total_performance = sum(self.strategy_performance.values())
        return {
            name: performance / total_performance
            for name, performance in self.strategy_performance.items()
        }

Custom Evolution Strategies

Strategy Configuration and Adjustment

Configuration File Example:

# costeer_config.yaml
costeer_settings:
  max_loop: 10
  knowledge_base_path: "./knowledge_base"
  new_knowledge_base_path: "./new_knowledge"
  evolving_version: 2

evolution_strategy:
  population_size: 50
  mutation_rate: 0.1
  crossover_rate: 0.8
  selection_method: "tournament"
  tournament_size: 5

rag_settings:
  embedding_model: "text-embedding-3-small"
  similarity_threshold: 0.75
  max_retrieved_docs: 10
  rerank_top_k: 5

evaluation_settings:
  metrics: ["accuracy", "f1_score", "auc_roc"]
  cross_validation_folds: 5
  test_ratio: 0.2

knowledge_management:
  auto_cleanup: true
  cleanup_threshold: 0.3
  knowledge_update_frequency: 100
  max_knowledge_items: 10000

Custom Evolution Strategy Implementation:

class CustomEvolutionStrategy(EvolvingStrategy):
    """Custom evolution strategy"""

    def __init__(self, config: dict):
        super().__init__()
        self.config = config
        self.population_size = config.get('population_size', 50)
        self.mutation_rate = config.get('mutation_rate', 0.1)
        self.crossover_rate = config.get('crossover_rate', 0.8)

    def initialize_population(self, size: int = None):
        """Initialize population"""
        size = size or self.population_size
        population = []

        for _ in range(size):
            individual = self.create_random_individual()
            population.append(individual)

        return population

    def evolve_generation(self, population, fitness_scores):
        """Evolve one generation"""
        new_population = []

        # Elite selection - preserve best individuals
        elite_count = int(len(population) * 0.1)
        elite_indices = np.argsort(fitness_scores)[-elite_count:]
        for idx in elite_indices:
            new_population.append(population[idx])

        # Generate new individuals through crossover and mutation
        while len(new_population) < len(population):
            # Select parents
            parent1 = self.tournament_selection(population, fitness_scores)
            parent2 = self.tournament_selection(population, fitness_scores)

            # Crossover
            if random.random() < self.crossover_rate:
                child1, child2 = self.crossover(parent1, parent2)
            else:
                child1, child2 = parent1, parent2

            # Mutation
            if random.random() < self.mutation_rate:
                child1 = self.mutate(child1)
            if random.random() < self.mutation_rate:
                child2 = self.mutate(child2)

            new_population.extend([child1, child2])

        return new_population[:len(population)]

    def crossover(self, parent1, parent2):
        """Crossover operation"""
        # Implement specific crossover logic
        # Parameter crossover example here
        child1_params = {}
        child2_params = {}

        for key in parent1.parameters.keys():
            if random.random() < 0.5:
                child1_params[key] = parent1.parameters[key]
                child2_params[key] = parent2.parameters[key]
            else:
                child1_params[key] = parent2.parameters[key]
                child2_params[key] = parent1.parameters[key]

        child1 = parent1.copy()
        child2 = parent2.copy()
        child1.parameters = child1_params
        child2.parameters = child2_params

        return child1, child2

    def mutate(self, individual):
        """Mutation operation"""
        mutated = individual.copy()

        for key, value in mutated.parameters.items():
            if random.random() < self.mutation_rate:
                if isinstance(value, (int, float)):
                    # Gaussian mutation
                    noise = np.random.normal(0, 0.1 * abs(value))
                    mutated.parameters[key] = value + noise
                elif isinstance(value, str):
                    # String mutation (e.g., model names)
                    mutated.parameters[key] = self.mutate_string(value)

        return mutated

Evaluator Customization

Multi-Metric Evaluator:

class MultiMetricEvaluator(RAGEvaluator):
    """Multi-metric evaluator"""

    def __init__(self, metrics_config: dict):
        super().__init__()
        self.metrics_config = metrics_config
        self.metric_weights = metrics_config.get('weights', {})
        self.metric_calculators = self._init_metric_calculators()

    def evaluate(self, experiment: Experiment) -> Dict[str, float]:
        """Multi-metric evaluation"""
        metrics = {}

        for metric_name, calculator in self.metric_calculators.items():
            try:
                score = calculator.calculate(experiment)
                metrics[metric_name] = score
            except Exception as e:
                logger.warning(f"Metric {metric_name} calculation failed: {e}")
                metrics[metric_name] = 0.0

        # Calculate weighted composite score
        weighted_score = self._calculate_weighted_score(metrics)
        metrics['weighted_score'] = weighted_score

        return metrics

    def _calculate_weighted_score(self, metrics: Dict[str, float]) -> float:
        """Calculate weighted composite score"""
        total_score = 0.0
        total_weight = 0.0

        for metric_name, score in metrics.items():
            if metric_name in self.metric_weights:
                weight = self.metric_weights[metric_name]
                total_score += score * weight
                total_weight += weight

        return total_score / total_weight if total_weight > 0 else 0.0

class CustomMetricCalculator:
    """Custom metric calculator"""

    def __init__(self, metric_name: str, calculation_func: callable):
        self.metric_name = metric_name
        self.calculation_func = calculation_func

    def calculate(self, experiment: Experiment) -> float:
        """Calculate metric value"""
        return self.calculation_func(experiment)

# Usage example
def sharpe_ratio_calculator(experiment):
    """Calculate Sharpe ratio"""
    returns = experiment.get_returns()
    if len(returns) == 0 or returns.std() == 0:
        return 0.0
    return returns.mean() / returns.std() * np.sqrt(252)

def max_drawdown_calculator(experiment):
    """Calculate maximum drawdown"""
    cumulative_returns = (1 + experiment.get_returns()).cumprod()
    running_max = cumulative_returns.expanding().max()
    drawdown = (cumulative_returns - running_max) / running_max
    return -drawdown.min()  # Return positive value

# Create custom evaluator
metrics_config = {
    'weights': {
        'sharpe_ratio': 0.4,
        'max_drawdown': 0.3,
        'information_ratio': 0.3
    }
}

evaluator = MultiMetricEvaluator(metrics_config)
evaluator.add_metric_calculator('sharpe_ratio', sharpe_ratio_calculator)
evaluator.add_metric_calculator('max_drawdown', max_drawdown_calculator)

Knowledge Base Management

Knowledge Base Optimization Strategy:

class KnowledgeBaseManager:
    """Knowledge base manager"""

    def __init__(self, config: dict):
        self.config = config
        self.knowledge_store = {}
        self.embedding_cache = {}
        self.access_frequency = defaultdict(int)
        self.last_access_time = {}

    def add_knowledge(self, knowledge_item: KnowledgeItem):
        """Add knowledge item"""
        # Check knowledge base size limit
        if len(self.knowledge_store) >= self.config.get('max_items', 10000):
            self._cleanup_knowledge()

        self.knowledge_store[knowledge_item.id] = knowledge_item
        self.embedding_cache[knowledge_item.id] = knowledge_item.to_embedding()
        self.last_access_time[knowledge_item.id] = datetime.now()

    def query_knowledge(
        self,
        query: str,
        top_k: int = 10,
        similarity_threshold: float = 0.7
    ) -> List[KnowledgeItem]:
        """Query relevant knowledge"""
        query_embedding = self._get_query_embedding(query)
        similarities = {}

        for item_id, item_embedding in self.embedding_cache.items():
            similarity = cosine_similarity(
                query_embedding.reshape(1, -1),
                item_embedding.reshape(1, -1)
            )[0][0]

            if similarity >= similarity_threshold:
                similarities[item_id] = similarity
                self.access_frequency[item_id] += 1
                self.last_access_time[item_id] = datetime.now()

        # Sort by similarity and return top-k
        sorted_items = sorted(
            similarities.items(),
            key=lambda x: x[1],
            reverse=True
        )[:top_k]

        return [self.knowledge_store[item_id] for item_id, _ in sorted_items]

    def _cleanup_knowledge(self):
        """Clean up knowledge base"""
        cleanup_count = int(len(self.knowledge_store) * 0.1)  # Clean 10%

        # Composite score based on access frequency and time
        scores = {}
        current_time = datetime.now()

        for item_id, knowledge_item in self.knowledge_store.items():
            frequency_score = self.access_frequency[item_id]
            time_score = (
                current_time - self.last_access_time[item_id]
            ).days
            effectiveness_score = knowledge_item.success_rate

            # Composite score (high frequency, recent, effective = high score)
            composite_score = (
                frequency_score * 0.4 +
                effectiveness_score * 0.4 -
                time_score * 0.2
            )
            scores[item_id] = composite_score

        # Remove lowest scored items
        items_to_remove = sorted(
            scores.items(),
            key=lambda x: x[1]
        )[:cleanup_count]

        for item_id, _ in items_to_remove:
            del self.knowledge_store[item_id]
            del self.embedding_cache[item_id]
            del self.access_frequency[item_id]
            del self.last_access_time[item_id]

        logger.info(f"Cleaned up {cleanup_count} knowledge items")

Multi-Agent Collaboration Mechanism

Research Agent Responsibilities

class ResearchAgent:
    """Research agent - responsible for hypothesis generation and strategy planning"""

    def __init__(self, knowledge_base: KnowledgeBase):
        self.knowledge_base = knowledge_base
        self.hypothesis_generator = HypothesisGenerator()
        self.strategy_planner = StrategyPlanner()

    def generate_hypotheses(self, problem_context: dict) -> List[Hypothesis]:
        """Generate research hypotheses"""
        # Retrieve relevant theories from knowledge base
        relevant_knowledge = self.knowledge_base.query(
            problem_context['description']
        )

        # Generate hypotheses based on domain knowledge
        hypotheses = []
        for knowledge_item in relevant_knowledge:
            hypothesis = self.hypothesis_generator.generate_from_knowledge(
                knowledge_item, problem_context
            )
            hypotheses.append(hypothesis)

        # Generate novel hypotheses
        novel_hypotheses = self.hypothesis_generator.generate_novel(
            problem_context, existing_hypotheses=hypotheses
        )
        hypotheses.extend(novel_hypotheses)

        return self.rank_hypotheses(hypotheses)

    def plan_research_strategy(self, hypothesis: Hypothesis) -> ResearchPlan:
        """Plan research strategy"""
        return self.strategy_planner.create_plan(
            hypothesis=hypothesis,
            available_resources=self.get_available_resources(),
            time_constraints=self.get_time_constraints()
        )

Development Agent Functions

class DevelopmentAgent:
    """Development agent - responsible for code implementation and testing validation"""

    def __init__(self, code_generator: CodeGenerator, validator: CodeValidator):
        self.code_generator = code_generator
        self.validator = validator
        self.implementation_cache = {}

    def implement_hypothesis(self, hypothesis: Hypothesis) -> Implementation:
        """Implement research hypothesis"""
        # Generate implementation code
        implementation_code = self.code_generator.generate_code(
            specification=hypothesis.specification,
            constraints=hypothesis.constraints
        )

        # Validate code correctness
        validation_result = self.validator.validate(implementation_code)

        if not validation_result.is_valid:
            # Fix code issues
            fixed_code = self.code_generator.fix_issues(
                implementation_code,
                validation_result.issues
            )
            implementation_code = fixed_code

        # Create implementation object
        implementation = Implementation(
            code=implementation_code,
            hypothesis=hypothesis,
            validation_result=validation_result
        )

        # Cache implementation
        self.implementation_cache[hypothesis.id] = implementation

        return implementation

    def optimize_implementation(
        self,
        implementation: Implementation,
        feedback: Feedback
    ) -> Implementation:
        """Optimize implementation based on feedback"""
        optimization_suggestions = self.analyze_feedback(feedback)

        optimized_code = self.code_generator.optimize_code(
            implementation.code,
            optimization_suggestions
        )

        return Implementation(
            code=optimized_code,
            hypothesis=implementation.hypothesis,
            parent_implementation=implementation
        )

Inter-Agent Communication Protocol

class AgentCommunicationProtocol:
    """Inter-agent communication protocol"""

    def __init__(self):
        self.message_queue = {}
        self.subscriptions = defaultdict(list)
        self.message_handlers = {}

    def register_agent(self, agent_id: str, agent: Agent):
        """Register agent"""
        self.message_queue[agent_id] = []
        agent.set_communication_protocol(self)

    def subscribe(self, agent_id: str, message_type: str):
        """Subscribe to message type"""
        self.subscriptions[message_type].append(agent_id)

    def publish(self, sender_id: str, message_type: str, message: dict):
        """Publish message"""
        # Add message metadata
        full_message = {
            'sender': sender_id,
            'type': message_type,
            'timestamp': datetime.now(),
            'content': message
        }

        # Send to all subscribers
        for subscriber_id in self.subscriptions[message_type]:
            if subscriber_id != sender_id:  # Don't send to self
                self.message_queue[subscriber_id].append(full_message)

    def get_messages(self, agent_id: str) -> List[dict]:
        """Get agent's messages"""
        messages = self.message_queue.get(agent_id, [])
        self.message_queue[agent_id] = []  # Clear queue
        return messages

class CollaborativeWorkflow:
    """Collaborative workflow"""

    def __init__(self, agents: List[Agent], protocol: AgentCommunicationProtocol):
        self.agents = {agent.id: agent for agent in agents}
        self.protocol = protocol
        self.workflow_state = WorkflowState.INITIALIZED

    def execute_collaborative_task(self, task: CollaborativeTask):
        """Execute collaborative task"""
        # Decompose task
        subtasks = self.decompose_task(task)

        # Assign subtasks to suitable agents
        task_assignments = self.assign_tasks(subtasks)

        # Execute subtasks
        results = {}
        for agent_id, assigned_tasks in task_assignments.items():
            agent = self.agents[agent_id]
            agent_results = agent.execute_tasks(assigned_tasks)
            results[agent_id] = agent_results

        # Coordinate and integrate results
        integrated_result = self.integrate_results(results)

        return integrated_result

    def coordinate_agents(self):
        """Coordinate agent activities"""
        # Process inter-agent messages
        for agent_id, agent in self.agents.items():
            messages = self.protocol.get_messages(agent_id)
            if messages:
                agent.process_messages(messages)

        # Check if coordination is needed
        coordination_needed = self.check_coordination_needs()
        if coordination_needed:
            self.perform_coordination()

---

This chapter demonstrates RD-Agent's advanced features and deep customization capabilities, including specialized functions of the quantitative finance agent, core mechanisms of the CoSTEER evolution framework, and implementation methods for multi-agent collaboration. The next chapter will demonstrate practical applications of these advanced features through real-world case studies.